{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forecasting with Chronos\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/autogluon/autogluon/blob/master/docs/tutorials/timeseries/forecasting-chronos.ipynb)\n",
    "[![Open In SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/autogluon/autogluon/blob/master/docs/tutorials/timeseries/forecasting-chronos.ipynb)\n",
    "\n",
    "AutoGluon-TimeSeries (AG-TS) now features [Chronos](https://github.com/amazon-science/chronos-forecasting), a family of pretrained time series forecasting models. Chronos models are based on language model architectures, and work by quantizing time series into buckets which are treated as tokens. Language models are then trained on these token sequences using cross-entropy loss. \n",
    "\n",
    "The current iteration of Chronos models, [available](https://huggingface.co/amazon/chronos-t5-large) on Hugging Face 🤗, is based on the T5 architecture and was trained on a large corpus of open-source time series data augmented with synthetic data generation techniques. The Chronos [paper](https://arxiv.org/abs/2403.07815) provides greater detail about the models and how they were trained. \n",
    "\n",
    "AG-TS provides a robust and easy way to use Chronos through the familiar `TimeSeriesPredictor` API.\n",
    "- Chronos can be combined with other forecasting models to build accurate ensembles using the `\"high_quality\"` and `\"best_quality\"` presets.\n",
    "- Alternatively, Chronos can be used as a standalone zero-shot model with presets such as `\"chronos_small\"` or `\"chronos_base\"`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "remove-cell",
     "skip-execution"
    ]
   },
   "outputs": [],
   "source": [
    "# We use uv for faster installation\n",
    "!pip install uv\n",
    "!uv pip install -q autogluon.timeseries --system\n",
    "!uv pip uninstall -q torchaudio torchvision torchtext --system # fix incompatible package versions on Colab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from autogluon.timeseries import TimeSeriesDataFrame, TimeSeriesPredictor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting Started with Chronos\n",
    "\n",
    "Chronos is available in 5 model sizes with different numbers of parameters: `tiny` (8M), `mini` (20M), `small` (46M), `base` (200M), and `large` (710M). Being a pretrained model for zero-shot forecasting, Chronos is different from other models available in AG-TS. \n",
    "Specifically, Chronos models do not really `fit` time series data. However, when `predict` is called, they carry out a relatively more expensive computation that scales linearly with the number of time series in the dataset. In this aspect, they behave like local statistical models such as ETS or ARIMA, where expensive computation happens during inference. Differently from statistical models, however, computation in the larger Chronos models requires an accelerator chip to run in a reasonable amount of time.\n",
    "\n",
    "The easiest way to get started with Chronos is through model-specific presets available in the `TimeSeriesPredictor`. As of v1.1, the `TimeSeriesPredictor.fit` method has a separate Chronos preset for each model size, such as `\"chronos_small\"` or `\"chronos_base\"`.\n",
    "\n",
    "Alternatively, Chronos can be combined with other time series models using presets `\"chronos_ensemble\"`, `\"chronos_large_ensemble\"`, `\"high_quality\"` and `\"best_quality\"`. More details about these presets are available in the documentation for [`TimeSeriesPredictor.fit`](https://auto.gluon.ai/stable/api/autogluon.timeseries.TimeSeriesPredictor.fit.html).\n",
    "\n",
    "Note that the model sizes `small` and higher require a GPU to run. However, models `tiny` and `mini` can be run on the CPU as well. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Let's work with a subset of the M4 competition data set to see Chronos-tiny in action."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item_id</th>\n",
       "      <th>timestamp</th>\n",
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       "      <th rowspan=\"5\" valign=\"top\">H1</th>\n",
       "      <th>1750-01-01 00:00:00</th>\n",
       "      <td>605.0</td>\n",
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       "      <th>1750-01-01 01:00:00</th>\n",
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       "      <th>1750-01-01 02:00:00</th>\n",
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       "      <th>1750-01-01 03:00:00</th>\n",
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       "      <th>1750-01-01 04:00:00</th>\n",
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      ],
      "text/plain": [
       "                             target\n",
       "item_id timestamp                  \n",
       "H1      1750-01-01 00:00:00   605.0\n",
       "        1750-01-01 01:00:00   586.0\n",
       "        1750-01-01 02:00:00   586.0\n",
       "        1750-01-01 03:00:00   559.0\n",
       "        1750-01-01 04:00:00   511.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = TimeSeriesDataFrame(\n",
    "    \"https://autogluon.s3.amazonaws.com/datasets/timeseries/m4_hourly_tiny/train.csv\"\n",
    ")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Beginning AutoGluon training...\n",
      "AutoGluon will save models to 'AutogluonModels/ag-20240416_084300'\n",
      "=================== System Info ===================\n",
      "AutoGluon Version:  1.1.0b20240415\n",
      "Python Version:     3.10.14\n",
      "Operating System:   Linux\n",
      "Platform Machine:   x86_64\n",
      "Platform Version:   #1 SMP Tue Mar 26 20:11:48 UTC 2024\n",
      "CPU Count:          32\n",
      "GPU Count:          4\n",
      "Memory Avail:       221.73 GB / 239.85 GB (92.4%)\n",
      "Disk Space Avail:   104.55 GB / 984.21 GB (10.6%)\n",
      "===================================================\n",
      "Setting presets to: chronos_tiny\n",
      "\n",
      "Fitting with arguments:\n",
      "{'enable_ensemble': True,\n",
      " 'eval_metric': WQL,\n",
      " 'hyperparameters': {'Chronos': {'model_path': 'tiny'}},\n",
      " 'known_covariates_names': [],\n",
      " 'num_val_windows': 1,\n",
      " 'prediction_length': 24,\n",
      " 'quantile_levels': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],\n",
      " 'random_seed': 123,\n",
      " 'refit_every_n_windows': 1,\n",
      " 'refit_full': False,\n",
      " 'skip_model_selection': True,\n",
      " 'target': 'target',\n",
      " 'verbosity': 2}\n",
      "\n",
      "Inferred time series frequency: 'H'\n",
      "Provided train_data has 13520 rows, 20 time series. Median time series length is 676 (min=676, max=676). \n",
      "\n",
      "Provided data contains following columns:\n",
      "\ttarget: 'target'\n",
      "\n",
      "AutoGluon will gauge predictive performance using evaluation metric: 'WQL'\n",
      "\tThis metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.\n",
      "===================================================\n",
      "\n",
      "Starting training. Start time is 2024-04-16 08:43:02\n",
      "Models that will be trained: ['Chronos[tiny]']\n",
      "Training timeseries model Chronos[tiny]. \n",
      "\t0.00    s     = Training runtime\n",
      "Training complete. Models trained: ['Chronos[tiny]']\n",
      "Total runtime: 0.01 s\n",
      "Best model: Chronos[tiny]\n"
     ]
    }
   ],
   "source": [
    "prediction_length = 24\n",
    "train_data, test_data = data.train_test_split(prediction_length)\n",
    "\n",
    "predictor = TimeSeriesPredictor(prediction_length=prediction_length).fit(\n",
    "    train_data, presets=\"chronos_tiny\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As promised, Chronos does not take any time to `fit`. The `fit` call merely serves as a proxy for the `TimeSeriesPredictor` to do some of its chores under the hood, such as inferring the frequency of time series and saving the predictor's state to disk. \n",
    "\n",
    "Let's use the `predict` method to generate forecasts, and the `plot` method to visualize them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Model not specified in predict, will default to the model with the best validation score: Chronos[tiny]\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 2000x350 with 2 Axes>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = predictor.predict(train_data)\n",
    "predictor.plot(\n",
    "    data=data, \n",
    "    predictions=predictions, \n",
    "    item_ids=[\"H1\", \"H2\"],\n",
    "    max_history_length=200,\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuring for Performance\n",
    "\n",
    "Looks good! As with all large deep learning models, however, some fine-grained control of inference parameters can be needed to both optimize the speed and avoid out-of-memory issues on specific hardware. For this, we will need to dive a bit deeper, configuring `hyperparameters` of the `TimeSeriesPredictor` directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictor = TimeSeriesPredictor(prediction_length=prediction_length).fit(\n",
    "    train_data,\n",
    "    hyperparameters={\n",
    "        \"Chronos\": {\n",
    "            \"model_path\": \"tiny\",\n",
    "            \"batch_size\": 64,\n",
    "            \"device\": \"cpu\",\n",
    "        }\n",
    "    },\n",
    "    skip_model_selection=True,\n",
    "    verbosity=0,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2min 4s, sys: 42.4 s, total: 2min 47s\n",
      "Wall time: 11.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "predictions = predictor.predict(train_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above, we used the following configuration options for the `TimeSeriesPredictor`:\n",
    "- we set `skip_model_selection=True` to skip running backtests during `fit`, as we will only consider a single model.\n",
    "- in the `hyperparameters` for the Chronos model,\n",
    "    - `model_path` allows us to change the model size or select different pretrained weights. This parameter can be a model string like `tiny` or `base`, a Hugging Face path like `amazon/chronos-t5-mini`, or a path to a local folder with custom weights.\n",
    "    - `batch_size` configures the number of time series for which predictions are generated in parallel. \n",
    "    - `device` instructs Chronos to run the model on CPU.\n",
    "\n",
    "As we see, inference speed is slower on the CPU compared to the GPU, taking about 400ms per time series.\n",
    "To overcome this limitation, AutoGluon implementation of Chronos supports several deep learning compilers that can optimize model performance on CPUs.\n",
    "\n",
    "For example, we can set `optimization_strategy=\"openvino\"` to use the [OpenVINO](https://github.com/openvinotoolkit/openvino) compiler for Intel CPUs to speed up Chronos inference. Behind the scenes, AutoGluon will use Hugging Face [optimum](https://github.com/huggingface/optimum-intel) for this conversion.\n",
    "\n",
    "Note that this requires installing the optional OpenVINO dependency for AG-TS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -q \"autogluon.timeseries[chronos-openvino]\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To speed up the inference even further, we can `persist` the model after calling `fit`. The `TimeSeriesPredictor.persist` method tells AutoGluon to keep the Chronos model in device memory for fast, on-demand inference instead of loading the model from disk each time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "predictor = TimeSeriesPredictor(prediction_length=prediction_length).fit(\n",
    "    train_data,\n",
    "    hyperparameters={\n",
    "        \"Chronos\": {\n",
    "            \"model_path\": \"tiny\",\n",
    "            \"batch_size\": 64,\n",
    "            \"device\": \"cpu\",\n",
    "            \"optimization_strategy\": \"openvino\",\n",
    "        }\n",
    "    },\n",
    "    skip_model_selection=True,\n",
    "    verbosity=0,\n",
    ")\n",
    "predictor.persist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 8s, sys: 9.19 s, total: 1min 17s\n",
      "Wall time: 2.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "predictions = predictor.predict(train_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That reduced the inference time by ~3x!\n",
    "\n",
    "We could have also used the ONNX runtime by providing `optimization_strategy=\"onnx\"`. For a discussion of these and other hyperparameters of Chronos, see the Chronos model [documentation](forecasting-model-zoo.md)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FAQ\n",
    "\n",
    "\n",
    "#### How accurate is Chronos?\n",
    "\n",
    "In several independent evaluations we found Chronos to be effective in zero-shot forecasting. \n",
    "The accuracy of Chronos-large often exceeds statistical baseline models, and is often comparable to deep learning \n",
    "models such as `TemporalFusionTransformer` or `PatchTST`.\n",
    "\n",
    "#### What hardware do larger Chronos models require?\n",
    "\n",
    "We tested Chronos on AWS `g5.2xlarge` and `p3.2xlarge` instances that feature NVIDIA A10G and V100 GPUs, with at least 16GiB of GPU memory and 32GiB of main memory.\n",
    "\n",
    "#### Can I fine-tune Chronos?\n",
    "\n",
    "The current iteration of Chronos on AutoGluon does not support fine tuning, although we will provide this functionality in later versions of AutoGluon.\n",
    "\n",
    "#### Does Chronos work with covariates or features?\n",
    "\n",
    "The current iteration of Chronos does not support covariates or features, however we will provide this functionality in \n",
    "later versions. In the meanwhile, presets such as `chronos_ensemble` combine Chronos with models that do take advantage of features.\n",
    "\n",
    "#### Where can I ask specific questions on Chronos?\n",
    "\n",
    "The AutoGluon team are among the core developers of Chronos. So you can ask Chronos-related questions on AutoGluon channels such \n",
    "as the Discord [server](https://discord.gg/wjUmjqAc2N), or [GitHub](https://github.com/autogluon/autogluon). You can also join \n",
    "the discussion on the Chronos GitHub [page](https://github.com/amazon-science/chronos-forecasting/discussions)."
   ]
  }
 ],
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